Screening Perovskites from ABO(3) Combinations Generated by Constraint Satisfaction Techniques Using Machine Learning

[Image: see text] Perovskite oxides are attractive candidates for various scientific applications because of their outstanding structure flexibilities and attractive physical and chemical properties. However, labor-intensive and high-cost experimental and density functional theory calculation approaches are normally used to screen candidate perovskites. Herein, a machine learning method is employed to identify perovskites from ABO(3) combinations formulated as constraint satisfaction problems based on the restrictions of charge neutrality and Goldschmidt tolerance factor. By eliminating five features based on their correlation and importance, 16 features refined from 21 features are employed to describe 343 known ABO(3) compounds for perovskite formability and stability model training. It is found that the top three features for predicting formability are structural features of the A–O bond length, tolerance, and octahedral factors, whereas the top nine features for predicting the stability are elemental and structural features related to the B-site elements. The precision and recall of the two models are 0.983, 1.00 and 0.971, 0.943, respectively. The formability prediction model categorizes 2229 ABO(3) combinations into 1373 perovskites and 856 nonperovskites, whereas the stability prediction model distinguishes 430 stable perovskites from 1799 unstable ones. Three hundred thirty-eight combinations are recognized as both formable and stable perovskites for future investigation.